With cancer remaining as one of the main causes of deaths worldwide, many studies are undergoing the effort to look for a novel and potent anticancer drug. Nanoparticles (NPs) are one of the rising fields in research for anticancer drug development. One of the key advantages of using NPs for cancer therapy is its high flexibility for modification, hence additional properties can be added to the NPs in order to improve its anticancer action. Polymer has attracted considerable attention to be used as a material to enhance the bioactivity of the NPs. Nanogels, which are NPs cross-linked with hydrophilic polymer network have also exhibited benefits in anticancer application. The characteristics of these nanomaterials include non-toxic, environment-friendly, and variable physiochemical properties. Some other unique properties of polymers are also attributed by diverse methods of polymer synthesis. This then contributes to the unique properties of the nanodrugs. This review article provides an in-depth update on the development of polymer-assisted NPs and nanogels for cancer therapy. Topics such as the synthesis, usage, and properties of the nanomaterials are discussed along with their mechanisms and functions in anticancer application. The advantages and limitations are also discussed in this article.A real space understanding of the Su-Schrieffer-Heeger model of polyacetylene is introduced thanks to delocalization indices defined within the quantum theory of atoms in molecules. This approach enables to go beyond the analysis of electron localization usually enabled by topological insulator indices-such as IPR-enabling to differentiate between trivial and topological insulator phases. The approach is based on analyzing the electron delocalization between second neighbors, thus highlighting the relevance of the sublattices induced by chiral symmetry. Moreover, the second neighbor delocalization index, δi,i+2, also enables to identify the presence of chirality and when it is broken by doping or by eliminating atom pairs (as in the case of odd number of atoms chains). Hints to identify bulk behavior thanks to δ1,3 are also provided. Overall, we present a very simple, orbital invariant visualization tool that should help the analysis of chirality (independently of the crystallinity of the system) as well as spreading the concepts of topological behavior thanks to its relationship with well-known chemical concepts.We previously observed beneficial effects of a carbohydrate-reduced, high-protein (CRHP) diet on cardiovascular risk markers in patients with type 2 diabetes mellitus (T2DM) in a crossover 2 × 6-week trial, when all food was provided to subjects as ready-to-eat meals. Here, we report the results from a 6-month open label extension 28 patients with T2DM were instructed to self-prepare the CRHP diet with dietetic guidance. At weeks 0, 6, 12, and 36, fasting and postprandial (4-h meal test) blood samples were collected for measurements of total, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol, triacylglycerol (TG), apolipoproteins A1 and B, non-esterified fatty acids (NEFA), C-reactive protein (CRP), tumor necrosis factor-α (TNF-α), and interleukin-6. Diurnal blood pressure and heart rate were also assessed. At the end of the study (week 36), concentrations of fasting total and LDL-cholesterol, fasting and postprandial NEFA and TG, and fasting apolipoprotein-B, CRP and TNF-α concentrations were significantly lower compared with week 0 (p less then 0.05). A significant decrease in diurnal heart rate was also observed. From week 12 to 36, an increase in HDL-cholesterol and apolipoprotein-A1 concentrations and a further reduction in fasting and postprandial NEFA (p less then 0.05) were found. These changes were independent of minor fluctuations in body weight. We conclude that the substitution of dietary carbohydrate for protein and fat has beneficial effects on several cardiovascular risk markers in patients with T2DM, which are maintained or augmented over the next 6 months when patients select and prepare the CRHP diet on their own in a dietitian-supported setting.Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer's disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research groups and ours, the comprehensive and sequential application of ML to provide an exact estimation of the actual ADAOO, instead of a high-confidence-interval ADAOO that may fall, remains to be explored. Here, we assessed the performance of ML algorithms for predicting ADAOO using two AD cohorts with early-onset familial AD and with late-onset sporadic AD, combining genetic and demographic variables. Performance of ML algorithms was assessed using the root mean squared error (RMSE), the R-squared (R2), and the mean absolute error (MAE) with a 10-fold cross-validation procedure. For predicting ADAOO in familial AD, boosting-based ML algorithms performed the best. In the sporadic cohort, boosting-based ML algorithms performed best in the training data set, while regularization methods best performed for unseen data. ML algorithms represent a feasible alternative to accurately predict ADAOO with little human intervention. Future studies may include predicting the speed of cognitive decline in our cohorts using ML.Lattice structures possess many superior properties over solid materials and conventional structures. Application-oriented lattice structure designs have become a choice in many industries, such as aerospace, automotive applications, construction, biomedical applications, and footwear. However, numerical and empirical analyses are required to predict mechanical behavior under different boundary conditions. In this article, a novel surface-based structure named O-surface structure is designed and inspired by existing Triply Periodic Minimal Surface morphologies in a particular sea urchin structure. For comparison, both structures were designed with two different height configurations and investigated for mechanical performance in terms of compression, local buckling, global buckling, and post-buckling behavior. Both simulation and experimental methods were carried out to reveal these aforementioned properties of samples fabricated by multi jet fusion technology. https://www.selleckchem.com/products/sgi-110.html The sea urchin structure exhibited better mechanical strength than its counterpart, with the same relative density almost two-folds higher in the compressive response.